{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from tensorflow.keras.layers import UpSampling2D, Conv2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DCGAN():\n",
    "    def __init__(self, rows, cols, channels, z = 10):\n",
    "        # Input shape\n",
    "        self.img_rows = rows\n",
    "        self.img_cols = cols\n",
    "        self.channels = channels\n",
    "        self.img_shape = (self.img_rows, self.img_cols, self.channels)\n",
    "        self.latent_dim = z\n",
    "\n",
    "        optimizer = Adam(0.0002, 0.5)\n",
    "\n",
    "        # Build and compile the discriminator\n",
    "        self.discriminator = self.build_discriminator()\n",
    "        self.discriminator.compile(loss='binary_crossentropy',\n",
    "            optimizer=optimizer,\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "        # Build the generator\n",
    "        self.generator = self.build_generator()\n",
    "\n",
    "        # The generator takes noise as input and generates imgs\n",
    "        z = Input(shape=(self.latent_dim,))\n",
    "        img = self.generator(z)\n",
    "\n",
    "        # For the combined model we will only train the generator\n",
    "        self.discriminator.trainable = False\n",
    "\n",
    "        # The discriminator takes generated images as input and determines validity\n",
    "        valid = self.discriminator(img)\n",
    "\n",
    "        # The combined model  (stacked generator and discriminator)\n",
    "        # Trains the generator to fool the discriminator\n",
    "        self.combined = Model(z, valid)\n",
    "        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
    "\n",
    "    def build_generator(self):\n",
    "\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Dense(128 * 7 * 7, activation=\"relu\", input_dim=self.latent_dim))\n",
    "        model.add(Reshape((7, 7, 128)))\n",
    "        model.add(UpSampling2D())\n",
    "        model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(UpSampling2D())\n",
    "        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(Conv2D(self.channels, kernel_size=3, padding=\"same\"))\n",
    "        model.add(Activation(\"tanh\"))\n",
    "\n",
    "        model.summary()\n",
    "\n",
    "        noise = Input(shape=(self.latent_dim,))\n",
    "        img = model(noise)\n",
    "\n",
    "        return Model(noise, img)\n",
    "\n",
    "    def build_discriminator(self):\n",
    "\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding=\"same\"))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n",
    "        model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(Conv2D(128, kernel_size=3, strides=2, padding=\"same\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(Conv2D(256, kernel_size=3, strides=1, padding=\"same\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "        model.summary()\n",
    "\n",
    "        img = Input(shape=self.img_shape)\n",
    "        validity = model(img)\n",
    "\n",
    "        return Model(img, validity)\n",
    "\n",
    "    def train(self, epochs, batch_size=256, save_interval=50):\n",
    "\n",
    "        # Load the dataset\n",
    "        (X_train, _), (_, _) = mnist.load_data()\n",
    "\n",
    "        # Rescale -1 to 1\n",
    "        X_train = X_train / 127.5 - 1.\n",
    "        X_train = np.expand_dims(X_train, axis=3)\n",
    "\n",
    "        # Adversarial ground truths\n",
    "        valid = np.ones((batch_size, 1))\n",
    "        fake = np.zeros((batch_size, 1))\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Discriminator\n",
    "            # ---------------------\n",
    "\n",
    "            # Select a random half of images\n",
    "            idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
    "            imgs = X_train[idx]\n",
    "\n",
    "            # Sample noise and generate a batch of new images\n",
    "            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))\n",
    "            gen_imgs = self.generator.predict(noise)\n",
    "\n",
    "            # Train the discriminator (real classified as ones and generated as zeros)\n",
    "            d_loss_real = self.discriminator.train_on_batch(imgs, valid)\n",
    "            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)\n",
    "            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Generator\n",
    "            # ---------------------\n",
    "\n",
    "            # Train the generator (wants discriminator to mistake images as real)\n",
    "            g_loss = self.combined.train_on_batch(noise, valid)\n",
    "\n",
    "            # Plot the progress\n",
    "            print (\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[1], g_loss))\n",
    "\n",
    "            # If at save interval => save generated image samples\n",
    "            if epoch % save_interval == 0:\n",
    "                self.save_imgs(epoch)\n",
    "\n",
    "    def save_imgs(self, epoch):\n",
    "        r, c = 5, 5\n",
    "        noise = np.random.normal(0, 1, (r * c, self.latent_dim))\n",
    "        gen_imgs = self.generator.predict(noise)\n",
    "\n",
    "        # Rescale images 0 - 1\n",
    "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "        fig, axs = plt.subplots(r, c)\n",
    "        cnt = 0\n",
    "        for i in range(r):\n",
    "            for j in range(c):\n",
    "                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')\n",
    "                axs[i,j].axis('off')\n",
    "                cnt += 1\n",
    "        fig.savefig(\"images/dcgan_mnist_%d.png\" % epoch)\n",
    "        plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 14, 14, 32)        320       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 7, 7, 64)          18496     \n",
      "_________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2 (Batc (None, 8, 8, 64)          256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 4, 4, 128)         73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_1 (Ba (None, 4, 4, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 256)         295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_2 (Ba (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 4097      \n",
      "=================================================================\n",
      "Total params: 393,729\n",
      "Trainable params: 392,833\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 6272)              68992     \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d (UpSampling2D) (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 14, 14, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_3 (Ba (None, 14, 14, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 64)        73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_4 (Ba (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 28, 28, 1)         577       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 291,713\n",
      "Trainable params: 291,329\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0701 09:44:52.290865 140117499188992 training.py:2061] Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "W0701 09:44:53.914172 140117499188992 training.py:2061] Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [D loss: 1.032658, acc.: 37.89%] [G loss: 0.711535]\n",
      "1 [D loss: 0.576665, acc.: 69.73%] [G loss: 0.979307]\n",
      "2 [D loss: 0.303214, acc.: 90.82%] [G loss: 0.930681]\n",
      "3 [D loss: 0.315156, acc.: 88.67%] [G loss: 0.627017]\n",
      "4 [D loss: 0.489921, acc.: 74.41%] [G loss: 0.782907]\n",
      "5 [D loss: 0.523139, acc.: 72.66%] [G loss: 1.326728]\n",
      "6 [D loss: 0.475447, acc.: 77.54%] [G loss: 1.007548]\n",
      "7 [D loss: 0.257883, acc.: 91.02%] [G loss: 0.325832]\n",
      "8 [D loss: 0.176714, acc.: 97.46%] [G loss: 0.209266]\n",
      "9 [D loss: 0.147136, acc.: 97.46%] [G loss: 0.216131]\n",
      "10 [D loss: 0.242347, acc.: 91.41%] [G loss: 0.443748]\n",
      "11 [D loss: 0.176513, acc.: 97.07%] [G loss: 0.604994]\n",
      "12 [D loss: 0.137959, acc.: 98.05%] [G loss: 0.190660]\n",
      "13 [D loss: 0.249862, acc.: 91.02%] [G loss: 0.144790]\n",
      "14 [D loss: 0.489584, acc.: 75.39%] [G loss: 0.457229]\n",
      "15 [D loss: 0.925210, acc.: 49.61%] [G loss: 1.062546]\n",
      "16 [D loss: 1.012178, acc.: 41.60%] [G loss: 1.003114]\n",
      "17 [D loss: 0.893808, acc.: 52.73%] [G loss: 1.039699]\n",
      "18 [D loss: 0.727307, acc.: 60.35%] [G loss: 1.025648]\n",
      "19 [D loss: 0.824906, acc.: 59.38%] [G loss: 0.856713]\n",
      "20 [D loss: 0.834537, acc.: 58.01%] [G loss: 0.682622]\n",
      "21 [D loss: 0.751115, acc.: 60.55%] [G loss: 0.359860]\n",
      "22 [D loss: 0.738925, acc.: 62.11%] [G loss: 0.170193]\n",
      "23 [D loss: 0.811012, acc.: 56.84%] [G loss: 0.371156]\n",
      "24 [D loss: 1.119386, acc.: 41.60%] [G loss: 1.398954]\n",
      "25 [D loss: 1.153905, acc.: 41.80%] [G loss: 1.795076]\n",
      "26 [D loss: 0.998260, acc.: 44.53%] [G loss: 1.813429]\n",
      "27 [D loss: 0.847873, acc.: 55.08%] [G loss: 1.864049]\n",
      "28 [D loss: 0.662979, acc.: 64.65%] [G loss: 1.985406]\n",
      "29 [D loss: 0.911325, acc.: 48.63%] [G loss: 1.675916]\n",
      "30 [D loss: 0.871777, acc.: 50.39%] [G loss: 1.339690]\n",
      "31 [D loss: 1.118890, acc.: 37.11%] [G loss: 1.287196]\n",
      "32 [D loss: 1.085539, acc.: 37.50%] [G loss: 1.730677]\n",
      "33 [D loss: 1.016040, acc.: 43.75%] [G loss: 1.632177]\n",
      "34 [D loss: 0.873514, acc.: 49.02%] [G loss: 1.462371]\n",
      "35 [D loss: 0.610971, acc.: 67.19%] [G loss: 1.398811]\n",
      "36 [D loss: 0.519673, acc.: 74.02%] [G loss: 1.225103]\n",
      "37 [D loss: 0.424595, acc.: 80.66%] [G loss: 0.754829]\n",
      "38 [D loss: 0.280285, acc.: 90.43%] [G loss: 0.465685]\n",
      "39 [D loss: 0.227862, acc.: 93.75%] [G loss: 0.343935]\n",
      "40 [D loss: 0.286534, acc.: 89.45%] [G loss: 0.422989]\n",
      "41 [D loss: 0.377889, acc.: 85.35%] [G loss: 0.637643]\n",
      "42 [D loss: 0.517162, acc.: 75.20%] [G loss: 0.749467]\n",
      "43 [D loss: 1.000040, acc.: 46.09%] [G loss: 1.032625]\n",
      "44 [D loss: 1.071392, acc.: 41.60%] [G loss: 1.065709]\n",
      "45 [D loss: 1.307123, acc.: 29.30%] [G loss: 1.208431]\n",
      "46 [D loss: 1.063518, acc.: 40.43%] [G loss: 1.362892]\n",
      "47 [D loss: 1.055987, acc.: 42.19%] [G loss: 1.096976]\n",
      "48 [D loss: 0.804042, acc.: 55.47%] [G loss: 1.018582]\n",
      "49 [D loss: 0.771061, acc.: 54.69%] [G loss: 1.035223]\n",
      "50 [D loss: 0.944851, acc.: 44.14%] [G loss: 1.166667]\n",
      "51 [D loss: 1.104903, acc.: 37.30%] [G loss: 1.335862]\n",
      "52 [D loss: 1.117883, acc.: 36.13%] [G loss: 1.351016]\n",
      "53 [D loss: 1.075584, acc.: 36.91%] [G loss: 1.369946]\n",
      "54 [D loss: 0.996678, acc.: 40.62%] [G loss: 1.346155]\n",
      "55 [D loss: 1.041082, acc.: 40.23%] [G loss: 1.263284]\n",
      "56 [D loss: 1.044470, acc.: 40.04%] [G loss: 1.382080]\n",
      "57 [D loss: 1.055838, acc.: 40.23%] [G loss: 1.423993]\n",
      "58 [D loss: 1.131721, acc.: 35.16%] [G loss: 1.285619]\n",
      "59 [D loss: 1.065221, acc.: 34.77%] [G loss: 1.454049]\n",
      "60 [D loss: 1.011806, acc.: 40.82%] [G loss: 1.408372]\n",
      "61 [D loss: 1.019234, acc.: 37.89%] [G loss: 1.339286]\n",
      "62 [D loss: 1.004157, acc.: 37.70%] [G loss: 1.293410]\n",
      "63 [D loss: 0.937149, acc.: 42.38%] [G loss: 1.263603]\n",
      "64 [D loss: 0.920623, acc.: 47.27%] [G loss: 1.239655]\n",
      "65 [D loss: 0.880720, acc.: 47.07%] [G loss: 1.352370]\n",
      "66 [D loss: 0.955383, acc.: 43.75%] [G loss: 1.447082]\n",
      "67 [D loss: 0.960565, acc.: 43.16%] [G loss: 1.300235]\n",
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      "69 [D loss: 1.031866, acc.: 37.89%] [G loss: 1.191407]\n",
      "70 [D loss: 1.041402, acc.: 38.09%] [G loss: 1.206638]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "884 [D loss: 0.744478, acc.: 47.27%] [G loss: 0.815750]\n",
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      "1031 [D loss: 0.745021, acc.: 46.09%] [G loss: 0.783980]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1175 [D loss: 0.740408, acc.: 47.66%] [G loss: 0.796698]\n",
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      "1319 [D loss: 0.727731, acc.: 49.41%] [G loss: 0.825573]\n",
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     "text": [
      "1607 [D loss: 0.746153, acc.: 43.75%] [G loss: 0.805225]\n",
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      "1751 [D loss: 0.720132, acc.: 48.05%] [G loss: 0.799727]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1896 [D loss: 0.733047, acc.: 45.31%] [G loss: 0.748140]\n",
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      "2003 [D loss: 0.726345, acc.: 46.48%] [G loss: 0.770653]\n",
      "2004 [D loss: 0.738820, acc.: 41.99%] [G loss: 0.800263]\n",
      "2005 [D loss: 0.733617, acc.: 47.46%] [G loss: 0.804201]\n",
      "2006 [D loss: 0.740978, acc.: 46.09%] [G loss: 0.840059]\n",
      "2007 [D loss: 0.744449, acc.: 43.16%] [G loss: 0.777096]\n",
      "2008 [D loss: 0.719234, acc.: 49.22%] [G loss: 0.829285]\n",
      "2009 [D loss: 0.716550, acc.: 48.63%] [G loss: 0.848845]\n",
      "2010 [D loss: 0.716057, acc.: 50.00%] [G loss: 0.802104]\n",
      "2011 [D loss: 0.722570, acc.: 48.05%] [G loss: 0.806656]\n",
      "2012 [D loss: 0.731548, acc.: 48.63%] [G loss: 0.802191]\n",
      "2013 [D loss: 0.718872, acc.: 50.59%] [G loss: 0.828417]\n",
      "2014 [D loss: 0.700527, acc.: 53.12%] [G loss: 0.825059]\n",
      "2015 [D loss: 0.751239, acc.: 44.34%] [G loss: 0.768358]\n",
      "2016 [D loss: 0.739665, acc.: 48.05%] [G loss: 0.783597]\n",
      "2017 [D loss: 0.723665, acc.: 49.02%] [G loss: 0.792275]\n",
      "2018 [D loss: 0.720967, acc.: 48.24%] [G loss: 0.823056]\n",
      "2019 [D loss: 0.725954, acc.: 47.85%] [G loss: 0.789220]\n",
      "2020 [D loss: 0.736589, acc.: 46.29%] [G loss: 0.786145]\n",
      "2021 [D loss: 0.720810, acc.: 46.48%] [G loss: 0.797591]\n",
      "2022 [D loss: 0.734298, acc.: 47.46%] [G loss: 0.807856]\n",
      "2023 [D loss: 0.729881, acc.: 50.78%] [G loss: 0.818907]\n",
      "2024 [D loss: 0.728680, acc.: 48.05%] [G loss: 0.807647]\n",
      "2025 [D loss: 0.723205, acc.: 49.61%] [G loss: 0.800185]\n",
      "2026 [D loss: 0.714864, acc.: 50.00%] [G loss: 0.823506]\n",
      "2027 [D loss: 0.744568, acc.: 43.36%] [G loss: 0.788760]\n",
      "2028 [D loss: 0.725713, acc.: 46.68%] [G loss: 0.810397]\n",
      "2029 [D loss: 0.730374, acc.: 44.73%] [G loss: 0.801674]\n",
      "2030 [D loss: 0.751399, acc.: 42.77%] [G loss: 0.770291]\n",
      "2031 [D loss: 0.730926, acc.: 46.68%] [G loss: 0.797634]\n",
      "2032 [D loss: 0.723579, acc.: 47.66%] [G loss: 0.766825]\n",
      "2033 [D loss: 0.724243, acc.: 46.68%] [G loss: 0.803970]\n",
      "2034 [D loss: 0.729008, acc.: 49.22%] [G loss: 0.796019]\n",
      "2035 [D loss: 0.733023, acc.: 46.68%] [G loss: 0.792826]\n",
      "2036 [D loss: 0.726054, acc.: 47.66%] [G loss: 0.755302]\n",
      "2037 [D loss: 0.744450, acc.: 41.41%] [G loss: 0.787477]\n",
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      "2039 [D loss: 0.719272, acc.: 47.85%] [G loss: 0.788044]\n"
     ]
    },
    {
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     "text": [
      "2040 [D loss: 0.708028, acc.: 51.76%] [G loss: 0.792455]\n",
      "2041 [D loss: 0.721462, acc.: 49.41%] [G loss: 0.787005]\n",
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      "2065 [D loss: 0.722212, acc.: 45.70%] [G loss: 0.799457]\n",
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      "2069 [D loss: 0.716676, acc.: 49.61%] [G loss: 0.765832]\n",
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      "2072 [D loss: 0.734882, acc.: 44.73%] [G loss: 0.788041]\n",
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      "2076 [D loss: 0.732647, acc.: 46.68%] [G loss: 0.778789]\n",
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      "2078 [D loss: 0.724579, acc.: 48.83%] [G loss: 0.780791]\n",
      "2079 [D loss: 0.713814, acc.: 50.98%] [G loss: 0.789897]\n",
      "2080 [D loss: 0.740181, acc.: 45.12%] [G loss: 0.779416]\n",
      "2081 [D loss: 0.721246, acc.: 48.63%] [G loss: 0.795620]\n",
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      "2083 [D loss: 0.740570, acc.: 44.34%] [G loss: 0.783045]\n",
      "2084 [D loss: 0.719433, acc.: 47.66%] [G loss: 0.790149]\n",
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      "2086 [D loss: 0.717622, acc.: 48.83%] [G loss: 0.801048]\n",
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      "2091 [D loss: 0.726551, acc.: 45.90%] [G loss: 0.766593]\n",
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      "2093 [D loss: 0.737578, acc.: 44.53%] [G loss: 0.762701]\n",
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      "2100 [D loss: 0.735195, acc.: 43.75%] [G loss: 0.783716]\n",
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     "text": [
      "2184 [D loss: 0.737261, acc.: 45.31%] [G loss: 0.785519]\n",
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    },
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     "text": [
      "2328 [D loss: 0.719905, acc.: 49.61%] [G loss: 0.784053]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2616 [D loss: 0.721483, acc.: 48.63%] [G loss: 0.764497]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "3049 [D loss: 0.731153, acc.: 44.53%] [G loss: 0.740646]\n",
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    },
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      "3193 [D loss: 0.724663, acc.: 47.46%] [G loss: 0.778280]\n",
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      "3337 [D loss: 0.722944, acc.: 46.88%] [G loss: 0.763068]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3769 [D loss: 0.715023, acc.: 45.70%] [G loss: 0.766288]\n",
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    },
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      "3913 [D loss: 0.708156, acc.: 50.59%] [G loss: 0.782203]\n",
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     "text": [
      "4057 [D loss: 0.714051, acc.: 47.27%] [G loss: 0.733270]\n",
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      "4201 [D loss: 0.709605, acc.: 47.85%] [G loss: 0.751678]\n",
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     "text": [
      "4346 [D loss: 0.719105, acc.: 48.63%] [G loss: 0.767247]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4490 [D loss: 0.716958, acc.: 46.09%] [G loss: 0.744671]\n",
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      "4634 [D loss: 0.709473, acc.: 47.07%] [G loss: 0.763231]\n",
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      "4778 [D loss: 0.716452, acc.: 42.38%] [G loss: 0.745524]\n",
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      "4850 [D loss: 0.710617, acc.: 47.27%] [G loss: 0.749718]\n",
      "4851 [D loss: 0.710106, acc.: 47.66%] [G loss: 0.749111]\n",
      "4852 [D loss: 0.716542, acc.: 44.14%] [G loss: 0.743028]\n",
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      "4855 [D loss: 0.719498, acc.: 43.36%] [G loss: 0.747550]\n",
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      "4881 [D loss: 0.705915, acc.: 48.24%] [G loss: 0.744901]\n",
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      "4898 [D loss: 0.709652, acc.: 49.61%] [G loss: 0.732669]\n",
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      "4904 [D loss: 0.719846, acc.: 42.97%] [G loss: 0.735535]\n",
      "4905 [D loss: 0.717864, acc.: 40.43%] [G loss: 0.747162]\n",
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      "4921 [D loss: 0.712144, acc.: 47.66%] [G loss: 0.719596]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4922 [D loss: 0.720632, acc.: 45.12%] [G loss: 0.742207]\n",
      "4923 [D loss: 0.719683, acc.: 42.58%] [G loss: 0.739047]\n",
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      "4927 [D loss: 0.718084, acc.: 44.92%] [G loss: 0.737757]\n",
      "4928 [D loss: 0.719513, acc.: 41.80%] [G loss: 0.727306]\n",
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      "4934 [D loss: 0.705145, acc.: 48.63%] [G loss: 0.738912]\n",
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      "4960 [D loss: 0.711288, acc.: 43.16%] [G loss: 0.740025]\n",
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      "4979 [D loss: 0.714906, acc.: 46.68%] [G loss: 0.742038]\n",
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      "4981 [D loss: 0.705469, acc.: 48.05%] [G loss: 0.745638]\n",
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      "4999 [D loss: 0.705127, acc.: 47.85%] [G loss: 0.756962]\n"
     ]
    }
   ],
   "source": [
    "dcgan = DCGAN(28,28,1)\n",
    "dcgan.train(epochs=5000, batch_size=256, save_interval=50)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tf2p0]",
   "language": "python",
   "name": "conda-env-tf2p0-py"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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